The present invention, in some embodiments thereof, relates to image processing and, more particularly, but not exclusively, to a method and system for detecting contours in an image.
A great deal of investigatory work and research has been expended in attempting to understand the biological visual system. Apart from the intrinsic value of the knowledge gained by such an understanding, it is hoped that the knowledge gained can be applied to produce man-made machines to simulate the biological visual system by a combination of opto-electronic devices and computer data processing techniques to thereby achieve the efficiency inherent in biological visual systems.
Among the properties of the biological visual system is perceptual grouping. The visual system segments optical input into regions that are separated by perceived contours or boundaries. This rapid, seemingly automatic, early step in visual processing is difficult to characterize, largely because many perceived contours have no obvious correlates in the optical input.
A contour in a pattern in a luminance map (e.g., an image) is generally defined as a spatial discontinuity in luminance. Although usually sufficient, however, such discontinuities are by no means necessary for sustaining perceived contours. Regions separated by visual contours also occur in the presence of: statistical differences in textural qualities (such as orientation, shape, density, or color), binocular matching of elements of differing disparities, accretion and deletion of texture elements in moving displays, and classical “subjective contours”. The extent to which the types of such perceived contours involve the same visual processes as those triggered by luminance contours is not obvious, although the former are certainly as perceptually real and generally as vivid as the latter.
The textural segmentation process is exquisitely context-sensitive. That is, a given texture element at a given location can be part of a variety of larger groupings, depending on what surrounds it. Indeed, the precise determination even of what acts as an element at a given location can depend on patterns at nearby locations.
Many computational models of illusory contours have been proposed. Representative examples include Grossberg et al., “Neural Dynamics of Form Perception: Boundary Completion, Illusory Figures, and Neon Spreading,” Psychological Review, 92(2), 173-211 (1985); Grossberg et al., “Neural dynamics of perceptual grouping: textures, boundaries, and emergent segmentations,” Perception and Psychophysics, 38(2), 141-171 (1985); Heitger et al., “Simulation of neural contour mechanisms: representing anomalous contours,” Image and Vision Computing, 16, 407-421 (1998); Gove et al., “Brightness perception, illusory contours, and corticogeniculate feedback,” Visual Neuroscience, 12, 1027-1052 (1995); Grossberg et al., “How does the cortex do perceptual grouping?” Trends in Neurosciences, 20(3), 106-111 (1997); S. Grossberg, “Cortical Dynamics of Three-Dimensional Figure-Ground Perception of Two-Dimensional Pictures,” Psychological Review, 104(3), 618-658 (1997); Ross et al., “Visual cortical mechanisms of perceptual grouping: interacting layers, networks, columns, and maps,” Neural Networks, 13, 571-588 (2000); Heitger et al., “Simulation of Neural Contour Mechanisms—from Simple to End-Stopped Cells,” Vision Res. 32, 963-981 (1992); F. Heitger, “A computational model of neural contour processing: Figure—ground segregation and illusory contours,” The Proceedings of the Seventh IEEE International Conference on Computer Vision 93, 32 (1999); Peterhans et al., “Simulation of neuronal responses defining depth order and contrast polarity at illusory contours in monkey area V2,” J. Comput Neurosci. 10, 195-211 (2001); and Finkel et al., “Integration of Distributed Cortical Systems by Reentry—a Computer-Simulation of Interactive Functionally Segregated Visual Areas,” J. Neurosci. 9, 3188-3208 (1989).
The above publications are inspired by physiological mechanisms, such as the receptive fields of simple, complex and hypercomplex (end-stopped) cells. These physiological models assume that the end-stopping mechanism is the main trigger of the illusory contour effect.